From Evaluating to Forecasting Performance: How to Turn Information Retrieval, Natural Language Processing and Recommender Systems into Predictive Sciences (Dagstuhl Perspectives Workshop 17442)

@article{Ferro2018FromET,
  title={From Evaluating to Forecasting Performance: How to Turn Information Retrieval, Natural Language Processing and Recommender Systems into Predictive Sciences (Dagstuhl Perspectives Workshop 17442)},
  author={Nicola Ferro and Norbert Fuhr and Gregory Grefenstette and Joseph A. Konstan and Pablo Castells and Elizabeth M. Daly and Thierry Declerck and Michael D. Ekstrand and Werner Geyer and Julio Gonzalo and Tsvi Kuflik and Krister Lind{\'e}n and Bernardo Magnini and Jian-Yun Nie and Raffaele Perego and Bracha Shapira and Ian Soboroff and Nava Tintarev and Karin M. Verspoor and Martijn C. Willemsen and Justin Zobel},
  journal={Dagstuhl Manifestos},
  year={2018},
  volume={7},
  pages={96-139}
}
We describe the state-of-the-art in performance modeling and prediction for Information Retrieval (IR), Natural Language Processing (NLP) and Recommender Systems (RecSys) along with its shortcomings and strengths. We present a framework for further research, identifying five major problem areas: understanding measures, performance analysis, making underlying assumptions explicit, identifying application features determining performance, and the development of prediction models describing the… CONTINUE READING

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